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Found 34 papers, 19 papers with code
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
In this work, we propose to automatically convert the background knowledge documents into document semantic graphs and then perform knowledge selection over such graphs.
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis
Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own, often for humor, provocation, or controversy.
The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucination
To address it, we propose a novel concept: knowledge overshadowing, where model's dominant knowledge can obscure less prominent knowledge during text generation, causing the model to fabricate inaccurate details.
Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation
Despite the remarkable capabilities of Large Language Models (LLMs) in various NLP tasks, they remain vulnerable to hallucinations due to their limited parametric knowledge and lack of domain-specific expertise.
SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Besides substantial performance gaps among agents (from Llama-3. 1 agent <= 3. 33% to Claude-3. 5-Sonnet >= 28. 18%), their consistently low collaboration willingness (<= 4. 86%) suggests fundamental limitations of existing LLM in CSE.
Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play
Complex news events, such as natural disasters and socio-political conflicts, require swift responses from the government and society.
Establishing Knowledge Preference in Language Models
Language models are known to encode a great amount of factual knowledge through pretraining.
Knowledge Overshadowing Causes Amalgamated Hallucination in Large Language Models
This phenomenon partially stems from training data imbalance, which we verify on both pretrained models and fine-tuned models, over a wide range of LM model families and sizes. From a theoretical point of view, knowledge overshadowing can be interpreted as over-generalization of the dominant conditions (patterns).
MACAROON: Training Vision-Language Models To Be Your Engaged Partners
Large vision-language models (LVLMs), while proficient in following instructions and responding to diverse questions, invariably generate detailed responses even when questions are ambiguous or unanswerable, leading to hallucinations and bias issues.
EVEDIT: Event-based Knowledge Editing with Deductive Editing Boundaries
The dynamic nature of real-world information necessitates efficient knowledge editing (KE) in large language models (LLMs) for knowledge updating.
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural language and formal language (code).
RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editor
In this paper, we present RESIN-EDITOR, an interactive event graph visualizer and editor designed for analyzing complex events.
Defining a New NLP Playground
The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history.
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
However, when it comes to information extraction - a classic task in natural language processing - most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users.
Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification
Event schemas are a form of world knowledge about the typical progression of events.
OpenPI-C: A Better Benchmark and Stronger Baseline for Open-Vocabulary State Tracking
Open-vocabulary state tracking is a more practical version of state tracking that aims to track state changes of entities throughout a process without restricting the state space and entity space.
Non-Sequential Graph Script Induction via Multimedia Grounding
To automate the induction of such graph scripts for given tasks, we propose to take advantage of loosely aligned videos of people performing the tasks.
Paxion: Patching Action Knowledge in Video-Language Foundation Models
Action knowledge involves the understanding of textual, visual, and temporal aspects of actions.
Ranked #39 on
Video Question Answering
on NExT-QA
(using extra training data)
GLEN: General-Purpose Event Detection for Thousands of Types
Finally, we perform error analysis and show that label noise is still the largest challenge for improving performance for this new dataset.
Human-in-the-Loop Schema Induction
Schema induction builds a graph representation explaining how events unfold in a scenario.
Open Relation and Event Type Discovery with Type Abstraction
To tackle this problem, we introduce the idea of type abstraction, where the model is prompted to generalize and name the type.
Open-Vocabulary Argument Role Prediction for Event Extraction
Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles.
Code4Struct: Code Generation for Few-Shot Event Structure Prediction
As a case study, we formulate Event Argument Extraction (EAE) as converting text into event-argument structures that can be represented as a class object using code.
Dynamic Global Memory for Document-level Argument Extraction
Extracting informative arguments of events from news articles is a challenging problem in information extraction, which requires a global contextual understanding of each document.
Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging.
Schema-Guided Event Graph Completion
Existing link prediction or graph completion methods have difficulty dealing with event graphs because they are usually designed for a single large graph such as a social network or a knowledge graph, rather than multiple small dynamic event graphs.
P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization
Our framework decomposes event detection into an identification task and a localization task.
Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion
Typical DocRE methods blindly take the full document as input, while a subset of the sentences in the document, noted as the evidence, are often sufficient for humans to predict the relation of an entity pair.
Ranked #5 on
Relation Extraction
on DocRED
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System
We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video).
Document-Level Event Argument Extraction by Conditional Generation
On the task of argument extraction, we achieve an absolute gain of 7. 6% F1 and 5. 7% F1 over the next best model on the RAMS and WikiEvents datasets respectively.
Document-level Event Extraction
Event Argument Extraction
+2
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction
We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations.
Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i. e., social relations.
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
With the massive number of repositories available, there is a pressing need for topic-based search.
